On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images

Khalil, Yasmina Al; Amirrajab, Sina; Lorenz, Cristian; Weese, Jürgen; Pluim, Josien P. W.; Breeuwer, Marcel

doi:10.1016/j.media.2022.102688

Cited by 23 publications

(10 citation statements)

References 61 publications

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“…GANs are versatile and effective tools for advancing medical imaging and analysis. Moreover, they have effectively improved the deep learning performance for various radiology tasks, including lesion detection, organ segmentation, and the prediction of patient outcomes, via data augmentation [ 84 85 86 87 88 89 ]. GANs have also been used in image registration to yield more accurate results.…”

Section: Potential and Application Of Generative Models In Clinical I...mentioning

confidence: 99%

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Kim,

Cho,

Jang

et al. 2024

Korean J Radiol

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The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

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Section: Potential and Application Of Generative Models In Clinical I...mentioning

confidence: 99%

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Kim,

Cho,

Jang

et al. 2024

Korean J Radiol

View full text Add to dashboard Cite

show abstract

“…Synthetic CT images generated from MRI data can be used for attenuation correction of PET images [ 45 ]. In addition, the use of synthetic data to balance medical datasets is widely recognized as a means of enhancing the performance of models in detecting, segmenting, and predicting medical conditions [ 116 117 118 119 ]. However, it is crucial to assess whether synthetic samples accurately capture the complexities and variations in real-world medical data.…”

Section: Overcoming the Challengesmentioning

confidence: 99%

Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning

Hong,

Jang,

Kyung

et al. 2023

Korean J Radiol

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Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.

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“…Al Khalil et al [24] propose a conditional GAN model, which increases segmentation performance on cardiac magnetic resonance images. The performance increase is especially noticeable when real and synthetic images are combined during training.…”

Section: Related Workmentioning

confidence: 99%

A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data

Schaudt,

Späte,

von Schwerin

et al. 2023

Bioengineering

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In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially increase the amount of available data in such a scenario. This synthetic data approach is evaluated on a a downstream classification task, predicting four causes for pneumonia as well as healthy cases on 1082 chest X-ray images. Quantitative and medical assessments show that a Generative Adversarial Network (GAN)-based approach significantly outperforms more recent diffusion-based approaches on this limited dataset with better image quality and pathological plausibility. We show that better image quality surprisingly does not translate to improved classification performance by evaluating five different classification models and varying the amount of additional training data. Class-specific metrics like precision, recall, and F1-score show a substantial improvement by using synthetic images, emphasizing the data rebalancing effect of less frequent classes. However, overall performance does not improve for most models and configurations, except for a DreamBooth approach which shows a +0.52 improvement in overall accuracy. The large variance of performance impact in this study suggests a careful consideration of utilizing generative models for limited data scenarios, especially with an unexpected negative correlation between image quality and downstream classification improvement.

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On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images

Cited by 23 publications

References 61 publications

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Updated Primer on Generative Artificial Intelligence and Large Language Models in Medical Imaging for Medical Professionals

Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning

A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data

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